Methods and systems for automated cementing and liner hanging

ABSTRACT

A method for controlling a well completion operation may include providing a controller access to a model that determines a process parameter relating to the well completion operation and estimating an operation parameter while conducting the well completion operation by using a sensor. A parameter adjustment relating to the well completion operation may be determined with the controller by using the model and the estimated operation parameter. The controller may generate a command relating to the well completion operation based on the determined parameter adjustment relating to the well completion operation. A related apparatus may include a completion system for conducting the well completion operation, a sensor for estimating the operation parameter, and a controller for generating the command as described above.

BACKGROUND OF THE DISCLOSURE 1. Field of the Disclosure

The present invention generally relates to systems and methods for intelligent and autonomous/semi-autonomous control of cementing and liner hanging operations.

2. Description of the Related Art

Boreholes, or wells, are drilled deep into the earth for many applications such as carbon dioxide sequestration, geothermal production, and hydrocarbon exploration and production. In all of the applications, the boreholes are drilled such that they pass through or allow access to a material (e.g., a gas or fluid) contained in a formation located below the earth's surface. Different types of tools and instruments may be disposed in the boreholes to perform various tasks and measurements. A well, e.g., for production, is generally completed by placing a casing (also referred to herein as a “liner” or “tubular”) in the wellbore. The spacing between the liner and the wellbore inside, referred to as the “annulus,” is then filled with cement. The liner and the cement may be perforated to allow the hydrocarbons to flow from the reservoirs to the surface via a production string installed inside the liner.

The disclosure herein provides intelligent control over one or more aspects of such completion operations.

SUMMARY OF THE DISCLOSURE

In aspects, the present disclosure provides a method for controlling a well completion operation. The method may include conducting the well completion operation, estimating at least one operation parameter while conducting the well completion operation using at least one sensor, using a controller to determine at least one parameter adjustment relating to the well completion operation, and generating a command relating to the well completion operation. The parameter adjustment determination may be done using the at least one model and the at least one estimated operating parameter. The command generation may be based on the determined at least one parameter adjustment.

In other aspects, the present disclosure provide an apparatus for controlling a well completion operation. The apparatus may include a completion system configured to conduct the well completion operation, at least one sensor configured to estimate at least one parameter while conducting the well completion operation, and a controller. The controller may have access to at least one model configured to determine a parameter relating to the well completion operation. The controller may be configured to determine at least one parameter adjustment relating to the well completion operation using the at least one model and the at least one estimated parameter and generate a command relating to the well completion operation based on the determined at least one parameter adjustment relating to the well completion operation.

Illustrative examples of some features of the disclosure thus have been summarized rather broadly in order that the detailed description thereof that follows may be better understood, and in order that the contributions to the art may be appreciated. There are, of course, additional features of the disclosure that will be described hereinafter and which will form the subject of the claims appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

For detailed understanding of the present disclosure, references should be made to the following detailed description of the preferred embodiment, taken in conjunction with the accompanying drawings, in which like elements have been given like numerals and wherein:

FIG. 1 shows a schematic diagram of a well with an illustrative completion system according to the present disclosure;

FIG. 2 is a flow chart illustrating one method according to the present disclosure;

FIG. 3 schematically illustrates a completion assembly during cementing;

FIG. 4A illustrates an exemplary torque versus height graph that may be used in conjunction with a model to control cementing operations according to one method according to the present disclosure; and

FIG. 4B illustrates an exemplary slow pump pressure (SPP) versus height graph that may be used in conjunction with a model to control cementing operations according to one method according to the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

In aspects, the present disclosure provides methods and related systems that intelligently guide, in an automated or semi-automated fashion, the various steps of completing a well by hanging and cementing a liner. Cementing can be a complicated procedure where the amount of cement, placement of the cement, and quality of the cement bond are important considerations. Embodiments of the present disclosure use a pre-defined process for such operations. A feedback loop may compare modeling versus real-time measurements to optimize the overall process. Additionally, operating parameters may be adjusted minute-by-minute and potential issues may be identified concurrently. Embodiments of the present disclosure use detailed pre-planning and real-time information to advise on what step(s) to take and what, if any, adjustments must be made to operating parameters. These actions may be fully automated, with human interaction only in case of unforeseen challenges. Alternatively, the systems may provide prompts/guidance that assist human operators.

In embodiments, the entire completion process, from liner hanging to cementing and clean up, may be optimized: from the simple timing of dropping darts that pressure-activate downhole tools and switching pumps off/on to complicated rotational speed (aka revolutions per minute, RPM)/axial movement variations along the process to accommodate varying geological or geometrical environments along the well path. Illustrative systems may compare simulation/modeling data with real-time sensor and offset data and adjust the procedures on the fly. It will be appreciated that such systems not only enhance the quality of the outcome, but also help to evaluate the final completion in view of certain quality goals such as cement channeling or completeness of a cleanup. Embodiments of the present disclosure may be used in conventional cementing/liner hanging operations, or in conjunction with a “one-trip” liner hanging and cementing tools.

In FIG. 1, there is shown an embodiment of a completion system 10 for performing liner hanging and cementing operations. The teachings of the present disclosure may be utilized in land, offshore or subsea applications. In FIG. 1, a laminated earth formation 12 is intersected by a borehole 14. A completion assembly 16 is conveyed via a work string 18 into the borehole 14. The completion assembly 16 includes a liner hanger assembly 20, which was run in using a suitable running tool (not shown) and installed. The work string 18 may be jointed drill pipe or coiled tubing, which may include conductors (not shown) for power and/or data for providing signal and/or power communication between the surface and downhole equipment. The completion assembly 16 may be configured in a conventional manner to flow cement 38 into an annulus 22 surrounding a liner 24 of the liner hanger assembly 20. The liner hanger assembly 20 may include a swivel to allow rotation, anchoring equipment, and actuating devices for activating the anchoring equipment. The completion system 10 may also include conventional rig equipment such as a mud pump 30, cement supply 32, a rotary power source 34 for rotating the work string 18, etc.

A controller 50 may be used in connection with the liner hanging and/or cementing activity. In some embodiments, the controller 50 uses preprogrammed algorithms, historical data, and real-time information in order to advise personnel on action(s) to take and/or automatically send commands to take such actions. These action may include, but are not limited to, varying RPM, pump rates, timing of dart insertion, and spacer sizes. The controller 50 may include conventional components such as microprocessors, a memory controller, a main memory, a network interface, a transceiver, etc. Near real-time or real-time information may be obtained using sensors that are distributed at surface and downhole locations. Surface sensors are labeled with numeral 52 and downhole sensors are labeled with numeral 54. Non-limiting examples of sensors 52, 54 include pressure sensors, temperature sensors, flow rate sensors, pump rate sensors, RPM sensors, vibration sensors, load sensors, e.g. hook load sensors, torque sensors, weight sensors, etc. A suitable communication system 56 may be used to transfer data and command signals. The communication system 56 may utilize wired pipe, optical fibers, EM signals, RF signals, acoustic signals, pressure pulses (e.g., mud pulses), etc.

Referring now to FIG. 2, there is shown a flow chart 70 illustrating one non-limiting method of the present disclosure. While the steps are serially arranged, it should be understood that some steps may be taken in parallel and that the order of certain steps can be varied. At step 72, a database is formed using historical information obtained during seismic surveys, logging-while drilling, lab results, wireline logging, etc. By “historical,” it is meant that the information is obtained prior to commencement of the liner hanging or cementing operation. The information in the database can relate to the formation (e.g., lithology, formation features, formation structure, geomechanical parameter, weakness or strength of formation zones, reservoir pressure, formation fluid characteristics, etc.), borehole features (e.g., diameter, rugosity, trajectory, etc.), and/or well equipment (e.g., operating parameters, pressure, temperature, flow rate, pump rate sensors, RPM, vibration, load, e.g. hook load, torque, weight, etc.). The database can also include information pertaining to equipment and materials used during the liner hanging and/or cementing operations; e.g., cement properties, spacer fluid properties, properties of equipment such as rubber, equipment behavior such as pressure spikes when darts seal are set, etc.

At step 74, real-time or near real-time information relating to one or more operating parameters is acquired. By “real-time” or “near real-time,” it is meant that such information is collected while liner hanging or cementing operations are ongoing. The operating parameters may be for surface and/or downhole equipment. Illustrative surface parameters include, but are not limited to, pump flow rates (e.g., drilling mud, spacer fluid, cement, etc.), pressure (e.g., bore pressure, annulus pressure, etc.), pressure, temperature, flow rate sensors, pump rate, RPM, vibration, load, e.g. hook load sensors, torque sensors, weight sensors, etc. and properties of returning fluids and/or entrained material such as mud cake or cement. Illustrative downhole parameters include, but are not limited to, pressure, temperature, flow rate, pump rate, RPM, vibration, load, e.g. hook load, torque, weight, etc. The measurements of such parameters sensors 52, 54 may provide absolute values and also the basis for estimating fluctuations or rates of change (e.g., for torque). The fluid returning to the surface may be analyzed to determine the quality/quantity of the cement slurry and the composition may be analyzed to determine the nature of the returning fluid (e.g., spacer fluid vs. drilling mud).

At step 76, the information from the database and acquired real-time information is used to build one or more models that may utilize algorithms to determine one or more subsequent process steps such as identifying a parameter or process step for adjustment. In one embodiment, the models are configured to estimate the condition or behavior of the completion equipment and/or wellbore environment and determine a course of action to optimize subsequent activity. The output of this set may be advice, command signals, and/or alarms. As used herein, the term “advice” is information communicated to a human operator, a “command signal” is a message that can be understood by a machine to perform or stop performing a given task or to adjust or otherwise amend a parameter that is used to perform a task, and an “alarm” is a special category of “advice” that indicates that an “out of norm” condition may be present. The model may comprise formation models, geo-mechanical models, hydraulics models, cement setting speed models, and models estimating the risk of channeling and stand-off annulus distribution. Such model generally are mathematically based algorithms and/or data maps providing one, two, or three dimensional distributions of a particular parameter in space or time that is either based on calculated data, measured data, or both. The environment and dynamics being simulated is shown in FIG. 3.

FIG. 3 schematically illustrates a liner hanger assembly 20 and a liner 24 at a well bottom 34 that traverses a formation having a weak zone 36. Cement 38 is shown being pumped out of the completion assembly 16 and into the annulus 22 between the outer surface of the completion assembly 16 and a borehole wall. As should be appreciated, several parameters influence the cementing operation, including rotation 40 of the liner assembly, a height 42 of the column of cement 38, the strength of the weak zone 36, and equivalent circulation density (ECD), which corresponds to the pressure of the fluid when circulating.

Two non-limiting examples of relationships that may be incorporated into a model are shown in FIGS. 4A, B. In FIG. 4A, there is shown torque (Tq) on the “x” axis 90 and height of the cement column (h) shown on the “y-axis” 92. An envelope 94 defines a set of values 96 for acceptable combinations of cement column height and torque. In FIG. 4B, there is shown stand pipe pressure (SPP) on the “x” axis 100 and height of the cement column (h) shown on the “y-axis” 102. An envelope 104 defines a set of values 106 for acceptable combinations of cement column height and SPP. The models may use these envelopes and values in conjunction with real time information from sensors to determine whether changes should be made to operating parameters (e.g., pressure, temperature, flow rate, pump rate, RPM, vibration, load, e.g. hook load, torque, weight, etc.) to improve efficiency, quality, speed, etc. In aspects, the outputs are constructed to optimize timing, reduce operational risks such as stuck pipe, and enable efficient excess cement removal. Thus, generally speaking, the models may use mathematical relationships, data sets, tables, etc. to characterize acceptable and/or unacceptable behavior, operating characteristics, or operations under pre-determined circumstances.

Generally, when pumping cement, the string/liner is rotated at low speeds. The selection of rotation speed is a balance between (i) keeping ECD low enough for the formation not to fracture (by keeping pump rate and RPM below certain thresholds), (ii) keeping cement channeling low by keeping pump rate at optimum rate which is usually high and RPM low or vice versa, (iii) and minimizing time spent by keeping pump rate high, especially given the cement setting speed. While a higher pump rate generally helps with channeling, by manipulating RPM and flow, one can influence the channeling effect by reducing or increasing these parameters independently. Other considerations may include fatigue of liner connection when experiencing too many total revolutions at a certain local hole curvature. The appropriate RPM, and other operating parameters, can change gradually over the course of the cement pumping procedure.

A relatively simple model may be used to control torque applied to the rotating liner. As cement sets, the rotating torque increases as the viscosity increases. The model may set a torque limit at which the running string must be pulled out to prevent the running string from being stuck in the well. In this particular case, the model may comprise not more than a single torque threshold value.

More complex models may be used to optimize and evaluate the actual cleanup effectiveness. Such a model would comprise parameters such as RPM, time, and/or the axial movement speed of the running string/liner, depth range, and number of repetitions of movement of a running string/liner. This model would make particular use of measurements of pressure and torque, and possibly require downhole sensing in the annulus. For instance, a communication system 56 at the top of the liner 20 may be utilized to communicate pressure, temperature, flow rate, pump rate, RPM, vibration, load, e.g. hook load, torque, weight, etc. Additionally, surface returns can be evaluated. The evaluation would primarily be used to characterize the nature of the returns and the distribution over time of the returns of cement slurry. Illustrative tools and sensor for such evaluation include a cuttings catcher, a measurement system for density, chemical or mineralogical composition, or similar devices (not shown).

Exemplary models may also be configured for cement channeling prediction. Rotating at low RPM may result in the cement not fully encircling the liner (i.e., no full circumference), which is due to low friction/low torque. Rotating at increasing RPM while evaluating torque changes can help predict gaps in cement column and when they are filled. For channeling detection, the torque when rotating at different rpms with the cement in the annulus is a function of the amount of circumference of the liner in contact with cement. In a horizontal application, the narrow annulus on the lowside frequently is not filled with the viscous cement, but needs rotation of a certain speed to squeeze the cement fully around the narrow lowside. This would show as an increase in torque that can be observed downhole or on surface. A non-limiting test may involve initiating a sequence of rotations at 10, 20, 30, and 40 RPM for a few minutes each, recording the torque increase and comparing the increases to modeled or offset torque curves of offset wells or earlier times with different location of the cement slurry. The visualized curves in comparison then may indicate the area of liner surface covered with cement.

Exemplary models may also be configured for mud cake removal prediction. The efficiency of spacer fluids in removing mud cake is influential for a cementing operation. Thus, a model may provide real time analysis of spacer fluid mud cake content volume/quality. This can be evaluated in real-time by checking the mud cake weight over time in the returns; e.g. by using a cuttings catcher. The evaluation may be inputted into the model to identify the depths at which residual mud cake exists. When combined with formation models, geo-mechanical models, hydraulics models, cement setting speed models, and models estimating the risk of channeling and stand-off annulus distribution, etc., this may then trigger the decision to extend or abbreviate the spacer pumping or to change quality or quantity of the cement pumped.

Depending on the model, illustrative input parameters include: open hole volume, formation integrity, cement volume, cement rheology, pump efficiencies, acceptable pump rates, acceptable rotation rates, and pressures (e.g., stand pipe pressure (SPP)), downhole pressure. These inputs may be used to automate critical operational parameters for cementing liners and casing downhole based on real-time simulations (RPM, SPP, flow rate). The models may simulate or predict relationships between RPM and flow rate, predict cement/spacer interface, predict cement column height (e.g., using torque and pressure), and/or estimate formation strength to manage flow rate (e.g., using equivalent circulating density). Illustrative actions may include starting/stopping rotation of the liner, when to apply weight or pull up the liner, when to actuate downhole mechanisms (e.g., dropping a dart).

It should be understood that a variety of control schemes may be used; e.g., complete machine automation, human control with machine generated advice, a hybrid of machine control with selective human intervention, etc. Thus, the modes discussed below are merely illustrative.

In a principally automated operating mode, the controller 50 sends command signals to surface and/or downhole equipment that adjust one or more operating set points. For instance, command signals may be sent to actuators for devices such as pumps, mixing equipment, valves, heaters, packers, top drive (or other surface rotary power source), etc. Command signal may include activation commands to activate actuators for such devices. For instance command signals may include an activation command to activate an actuator that is configured to allow pressurization of a packer or packer element in order to set the packer. Of course, human operators may be given the opportunity to stop or modify the action(s) to be taken. In a principally “advice” operating mode, the controller 50 presents a proposed course of action(s) to human operators, who then decide whether or not implement such action(s).

It should be appreciated that the methods and systems according to the present disclosure provide several advantages over conventional liner hanging and cementing techniques. These advantages include operational time savings due to faster procedures on average, repeatability of the process that yields higher quality of cementing, reduced need for training/building experience for operators, and the ability to build a database of historical friction/hydraulics curves useful to characterized operational issues, which can yield a better understanding for future applications.

The foregoing description is directed to particular embodiments of the present disclosure for the purpose of illustration and explanation. It will be apparent, however, to one skilled in the art that many modifications and changes to the embodiment set forth above are possible without departing from the scope of the disclosure. It is intended that the following claims be interpreted to embrace all such modifications and changes. 

What is claimed is:
 1. A method for controlling a well completion operation, comprising: forming at least one model configured to determine a process parameter relating to the well completion operation; conducting the well completion operation; estimating at least one operation parameter while conducting the well completion operation, the at least one operation parameter being estimated by at least one sensor; determining at least one parameter adjustment relating to the well completion operation, the determining being done with the controller, which uses the at least one model and the at least one estimated operation parameter; and generating a command relating to the well completion operation, the generating being done by the controller based on the determined at least one parameter adjustment.
 2. The method of claim 1, wherein the model is based on historical information, the historical information relating to at least one of: (i) a pump,(ii) cement, (iii) a spacer fluid, (iv), a liner hanger, (v) a formation, (vi) a drilling fluid. (vii) downhole pressure, (viii) a formation, (ix) a borehole.
 3. The method of claim 1, wherein the operation parameter is selected from at least one of: (i) a pressure, (ii) a temperature, (iii) a flow rate, (iv) a pump rate, (v) a rotational speed, (vi) a vibration, (vii) a hook load, (viii) a torque, (ix) a weight, (x) cement channeling, and (xi) presence of mud cake.
 4. The method of claim 1, wherein the well completion operation is selected from at least one of: (i) setting a liner hanger, (ii) pumping cement, (iii) rotating a liner, and (iv) setting a packer.
 5. The method of claim 1, wherein the determined at least one parameter adjustment relating to the well completion operation is: (i) a rotational speed of the liner, (ii) a pumping rate, and (iii) a choice of a downhole device that is to be actuated.
 6. The method of claim 5, wherein the downhole device is one of: (i) a communication module, (ii) an electrical power source, and (iii) a hydraulic power source.
 7. The method of claim 1, further comprising transmitting, by using the controller, at least one of: (i) a signal based on the command to an actuator, (ii) an advice to a human operator, and (iii) an alarm to a human operator.
 8. The method of claim 7, further comprising implementing the at least one parameter adjustment for the well completion operation by operating at least one tool one of: (i) a surface location, (ii) a downhole location, and (iii) the surface location and the downhole location.
 9. An apparatus for controlling a well completion operation, comprising: a completion system configured to conduct the well completion operation; at least one sensor configured to estimate at least one operation parameter while conducting the well completion operation; a controller having access to at least one model configured to determine a process parameter relating to the well completion operation, the controller being configured to: determine at least one parameter adjustment relating to the well completion operation using the at least one model and the at least one estimated operation parameter; and generate a command relating to the well completion operation based on the determined at least one parameter adjustment relating to the well completion operation.
 10. The apparatus of claim 9, wherein the model is based on historical information, the historical information relating to at least one of: (i) a pump,(ii) cement, (iii) a spacer fluid, (iv), a liner hanger, (v) a formation, (vi) a drilling fluid. (vii) downhole pressure, (viii) a formation, (ix) a borehole.
 11. The apparatus of claim 9, wherein the operation parameter is selected from at least one of: (i) a pressure, (ii) a temperature, (iii) a flow rate, (iv) a pump rate, (v) a rotational speed, (vi) a vibration, (vii) a hook load, (viii) a torque, (ix) a weight, (x) cement channeling, and (xi) presence of mud cake.
 12. The apparatus of claim 9, wherein the well completion operation is selected from at least one of: (i) setting a liner hanger, (ii) pumping cement, (iii) rotating a liner, and (iv) setting a packer.
 13. The apparatus of claim 9, wherein the determined at least one parameter adjustment relating to the well completion operation is: (i) a rotational speed of the liner, (ii) a pumping rate, and (iii) a choice of a downhole device that is to be actuated.
 14. The apparatus of claim 13, wherein the downhole device is one of: (i) a communication module, (ii) an electrical power source, and (iii) a hydraulic power source.
 15. The apparatus of claim 9, wherein the controller is further configured to transmit at least one of: (i) a signal based on the command to an actuator, (ii) an advice to a human operator, and (iii) an alarm to a human operator. 